{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:XES6CAEGNMJBO4JJHBGWFU4AVZ","short_pith_number":"pith:XES6CAEG","schema_version":"1.0","canonical_sha256":"b925e100866b12177129384d62d380ae7d27596043a1eee77c2e597d46305b65","source":{"kind":"arxiv","id":"2606.06415","version":1},"attestation_state":"computed","paper":{"title":"PolyGraphPy: A unified Python framework for atomistic simulation and machine learning-driven polymer design","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Jo\\~ao G. C. S. Duarte, Ketson R. M. dos Santos, Morgan Cencer, Shruti Venkatram, Traian Dumitric\\v{a}","submitted_at":"2026-06-04T17:17:35Z","abstract_excerpt":"Polymers are indispensable materials with applications ranging from electronics to medicine owing to their versatility, which can be tailored by adjusting their chemical composition and architecture. The design space for these compounds is vast and governed by factors such as monomer classes, copolymer configurations (e.g., linear, branched, random, and alternating), chain size, stoichiometry, and material properties (e.g., density, refractive index, solubility, and Poisson's ratio). Exploring this space requires efficient computational methodologies for polymer science. To address this challe"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.06415","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-06-04T17:17:35Z","cross_cats_sorted":[],"title_canon_sha256":"66c0d28d9dbc7edd58ae59caee896ddb2baaeefda449b0c922916127f8beb581","abstract_canon_sha256":"1212ab7b6faeffd6098a29b134c1d68648c08d3106a5cce620319f03dfb06579"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-05T01:15:44.909026Z","signature_b64":"XFBrr0vKopsq8vW5ZCWvqF9CODUaaB4BBxxPeMO6PPhocZoaNBCzyzHRKrJpY1AMTYN58Ai0nXqKOH56tSmRCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b925e100866b12177129384d62d380ae7d27596043a1eee77c2e597d46305b65","last_reissued_at":"2026-06-05T01:15:44.908552Z","signature_status":"signed_v1","first_computed_at":"2026-06-05T01:15:44.908552Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PolyGraphPy: A unified Python framework for atomistic simulation and machine learning-driven polymer design","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cond-mat.mtrl-sci","authors_text":"Jo\\~ao G. C. S. Duarte, Ketson R. M. dos Santos, Morgan Cencer, Shruti Venkatram, Traian Dumitric\\v{a}","submitted_at":"2026-06-04T17:17:35Z","abstract_excerpt":"Polymers are indispensable materials with applications ranging from electronics to medicine owing to their versatility, which can be tailored by adjusting their chemical composition and architecture. The design space for these compounds is vast and governed by factors such as monomer classes, copolymer configurations (e.g., linear, branched, random, and alternating), chain size, stoichiometry, and material properties (e.g., density, refractive index, solubility, and Poisson's ratio). Exploring this space requires efficient computational methodologies for polymer science. To address this challe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.06415","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.06415/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.06415","created_at":"2026-06-05T01:15:44.908630+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.06415v1","created_at":"2026-06-05T01:15:44.908630+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.06415","created_at":"2026-06-05T01:15:44.908630+00:00"},{"alias_kind":"pith_short_12","alias_value":"XES6CAEGNMJB","created_at":"2026-06-05T01:15:44.908630+00:00"},{"alias_kind":"pith_short_16","alias_value":"XES6CAEGNMJBO4JJ","created_at":"2026-06-05T01:15:44.908630+00:00"},{"alias_kind":"pith_short_8","alias_value":"XES6CAEG","created_at":"2026-06-05T01:15:44.908630+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XES6CAEGNMJBO4JJHBGWFU4AVZ","json":"https://pith.science/pith/XES6CAEGNMJBO4JJHBGWFU4AVZ.json","graph_json":"https://pith.science/api/pith-number/XES6CAEGNMJBO4JJHBGWFU4AVZ/graph.json","events_json":"https://pith.science/api/pith-number/XES6CAEGNMJBO4JJHBGWFU4AVZ/events.json","paper":"https://pith.science/paper/XES6CAEG"},"agent_actions":{"view_html":"https://pith.science/pith/XES6CAEGNMJBO4JJHBGWFU4AVZ","download_json":"https://pith.science/pith/XES6CAEGNMJBO4JJHBGWFU4AVZ.json","view_paper":"https://pith.science/paper/XES6CAEG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.06415&json=true","fetch_graph":"https://pith.science/api/pith-number/XES6CAEGNMJBO4JJHBGWFU4AVZ/graph.json","fetch_events":"https://pith.science/api/pith-number/XES6CAEGNMJBO4JJHBGWFU4AVZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XES6CAEGNMJBO4JJHBGWFU4AVZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XES6CAEGNMJBO4JJHBGWFU4AVZ/action/storage_attestation","attest_author":"https://pith.science/pith/XES6CAEGNMJBO4JJHBGWFU4AVZ/action/author_attestation","sign_citation":"https://pith.science/pith/XES6CAEGNMJBO4JJHBGWFU4AVZ/action/citation_signature","submit_replication":"https://pith.science/pith/XES6CAEGNMJBO4JJHBGWFU4AVZ/action/replication_record"}},"created_at":"2026-06-05T01:15:44.908630+00:00","updated_at":"2026-06-05T01:15:44.908630+00:00"}